Systems that use airborne platforms for imaging, such as satellites, aircraft and dirigibles, which are lighter than air at sea level, in order to remotely gather information are known. Passive spectral imaging of electromagnetic radiation, using natural sunlight as the illumination source, is capable of resolving information about elements of interest through a radiation obscuring media. Spectral imaging is a technology defined by the capturing and recording the electromagnetic radiation reflected and/or emitted from elements of interest. Conventional sensors focus on either the visible or infrared portions of the electromagnetic spectrum. The spectral response from the elements of interest, which are typically converted to electrical signals, such as by a charged coupled device (CCD), are discretized into numerous, narrow, contiguous wavebands. The number of wavebands used in multispectral imaging may vary from two relatively broad wavebands to many, narrower wavebands. By overlaying one waveband upon another, a three-dimensionally spectral data cube may be generated having spacial dimensions X and Y and a spectral dimension in the Z direction, for example.
Elements of interest are any features that the system is intended to detect by way of multispectral analysis. For example, bathymetry, temperature, salinity, natural and anthropogenic hydrocarbon spills and seepages, fresh water plumes, plankton blooms, pollutants, and tracking of fluorescent tracer dyes are examples of elements imaged by conventional spectral imaging. Spectral imaging may also be used for military applications to identify the location of elements of interest, including search and rescue, location of armaments and troops, detection of contaminants, observation of submerged or buried mines or other impediments, and tracking of supplies.
U.S. Pat. No. 6,008,492 (“the '492 patent”) entitled “Hyperspectral Imaging Method and Apparatus” describes the state of the art at the time the '492 patent was filed. The '492 patent does not disclose the use of light or imaging or the use of any second order filters. Instead, the '492 patent teaches numerically correcting for second order of effects, disclosing the use of merely a 10 bit CCD system, which has an inadequate dynamic range for use in oceanographically obscured environmental media.
Light detection and ranging (LIDAR) uses spectrally narrow pulses of an electromagnetic frequency band that are emitted by a sensor. The return signals are measured using time-of-flight techniques, such as time gating and ranging, which are used to determine the distances and/or the velocity of light in the media through which the spectrally narrow pulses pass.
U.S. Pat. No. 5,450,125 (“the '125 patent”) entitled “Spectrally Dispersive Imaging LIDAR System” discloses a LIDAR system that uses fluorescent decay properties and Raman emissions to collect spectral information. A cumulative, passive spectral response of the entire scene is returned to a narrow bandpass receiver. The spectral response is recorded without reserving the spatial information within the scene. Thus, the spectral response cannot be added as a layer of a multispectral image in a spectral data cube.
The complexity and variety of marine optical signals in coastal ocean areas has created a challenging environment for the development of remote sensing instrumentation and algorithms. To date, the most successful oceanic algorithms were based upon multispectral data streams and have been focused on the characterization of Case 1 type waters (waters where the optical constituents of the water co-vary with each other). See, H. R. Gordon and A. Morel, Remote assessment of ocean color for interpretation of satellite visible imagery, A review (Springer-Verlag, New York, 1983), p. 114; A. Morel, “Optical modeling of the upper ocean in relation to its biogenous matter content (Case I waters),” Journal of Geophysical Research 93(C9), 10, 749-710, 768 (1988); and H. R. Gordon, O. B. Brown, R. H. Evans, J. W. Brown, R. C. Smith, K. S. Baker, and D. K. Clark, “A semianalytic radiance model of ocean color,” Journal of Geophysical Research 93(D9), 10, 909-910, 924 (1988). While these algorithms have worked well for classifying marine water types, they have been less successful in describing near shore environments. See, C. Hu, K. L. Carder, and F. E. Muller-Karger, “Atmospheric Correction of SeaWiFS Imagery over Turbid Coastal Waters: A Practical Method,” Remote Sensing of Environment. Vol. 74, no. 2 (2000). The near shore environment has additional influences on the optical signals, which do not necessarily co-vary with signals produced by the ecology interactions. These additional signals include the influence of the bottom, which includes variations in the spectral signal and magnitude depending on the bathymetry and bottom type of the region. These bottom effects have temporal as well as spatial variations and include an impact due to seasonal changes in macrophyte coverage and re-suspended sediments. The algorithms are also hampered by colored degradation matter and sediments from terrestrial sources that contaminate the marine produced color signals.
The first step in any algorithm development for coastal optical remote sensing requires the accurate retrieval of water-leaving radiance, LW(λ), from sensor measured radiance. The sensor radiance signal is most often dominated by the atmospheric radiance additions and attenuations, such that LW(λ) is often just a small fraction of the measured photon density. The removal of the atmospheric interference in the water-leaving radiance signal requires a priori knowledge of a host of atmospheric constituents, e.g. water column water vapor, aerosol type and density, ozone concentration, etc. Without a priori knowledge, retrieval of these factors must be backed out from the spectral data stream itself, decreasing the degrees of freedom with which to resolve the water-leaving radiance signal. Additionally, the increased development along the world's coastal boundaries adds another layer of complexity for determining concentration and interactions between the marine and terrestrial aerosols. The atmospheric parameterization can change dramatically within a single scene in such a complex spectral environment.
Further complicating atmospheric correction in complex environment, such as a coastal marine environment, is the potential for bottom and suspended sediments. As opposed to deeper off shore waters, the assumption that these waters have no optical return in the near infra red is no longer valid. See, C. Hu, K. L. Carder, and F. E. Muller-Karger, “Atmospheric Correction of SeaWiFS Imagery over Turbid Coastal Waters: A Practical Method,” Remote Sensing of Environment. Vol. 74, no. 2 (2000); See, D. Siegel, M. Wang, S. Maritorena, and W. Robinson, “Atmospheric correction of satellite ocean color imagery: the black pixel assumption,” Applied Optics 39(21), 3582-3591 (2000); See, H. R. Gordon and D. K. Clark, “Clear water radiances for atmospheric correction of coastal zone color scanner imagery,” Applied Optics 20(24), 4175-4180 (1981); and See, K. Ruddick, F. Ovidio, and M. Rijkeboer, “Atmospheric correction of SeaWiFS imagery for turbid coastal and inland waters,” Applied Optics 39(6), 897-912 (2000). These considerations coupled with the dominance of the atmosphere's optical effects over the weak optical return from the coastal environment make atmospheric correction of these coastal areas very difficult.
Promising to deliver the extra information needed to properly handle such spectrally complex scenes, hyperspectral remote sensing emerged as a collection tool more than a decade ago. See, R. J. Birk and T. B. McCord, “Airborne Hyperspectral Sensor Systems,” IEEE AES Systems Magazine 9(10), 26-33 (1994) and See, K. L. Carder, P. Reinersman, R. F. Chen, F. Muller-Karger, C. O. Davis, and M. Hamilton, “AVIRIS calibration and application in coastal oceanic environments,” Remote Sensing of Environment 44, 205-216 (1993). Hyperspectral remote sensing data, with its numerous, narrow, contiguous wavebands, approximate the true electromagnetic signature of its target. See, Z. Lee, K. L. Carder, R. F. Chen, and T. G. Peacock, “Properties of the water column and bottom derived from Airborne Visible Infrared Imaging Spectrometer (AVIRIS) data,” Journal of Geophysical Research 106(C6), 11, 639-611, 652 (2001). With this new information, mathematical techniques originally developed in laboratory spectroscopy were applied to this data set in an attempt to characterize the imagery. There have been some recent efforts to use high spectral data in the mapping of the coastal zone. See, D. D. R. Kohler, “An evaluation of a derivative based hyperspectral bathymetric algorithm,” Dissertation Cornell University, Ithaca, N.Y., (2001); See, E. Louchard, R. Reid, F. Stephens, C. Davis, R. Leathers, and T. Downes, “Optical remote sensing of benthic habitats and bathymetry in coastal environments at Lee Stocking Island, Bahamas: A comparative spectral classification approach,” Limnol. Oceanogr. 48(1, part 2), 511-521 (2003); See, J. C. Sandidge and R. J. Holyer, “Coastal bathymetry from hyperspectral observations of water radiance,” Remote Sensing of Environment 65(3), 341-352 (1998); See, Z. Lee, K. L. Carder, C. D. Mobley, R. G. Steward, and J. S. Patch, “Hyperspectral remote sensing for shallow waters: 2. Deriving bottom depths and water properties by optimization,” Applied Optics 38(18), 3831-3843 (1999); and See, Z. Lee, K. L. Carder, C. D. Mobley, R. G. Steward, and J. S. Patch, “Hyperspectral remote sensing for shallow waters. 1. A semianalytical model,” Applied Optics vol. 37(no. 27), 6329-6338 (1998). However, the sensors used to collect the data for these previous studies suffer from sensitivity and calibration issues that become apparent in these low light scenes. The instruments' limitations require an on-site, vicarious calibration of the data to be useful in these environments. See, K. L. Carder, P. Reinersman, R. F. Chen, F. Muller-Karger, C. O. Davis, and M. Hamilton, “AVIRIS calibration and application in coastal oceanic environments,” Remote Sensing of Environment 44, 205-216 (1993). This in turn, reduces the applicability of these tools and techniques to other coastal areas, or even other water-types within the same image. In addition, remote sensing data analyses, such as temporal scene-to-scene comparisons, are virtually impossible to interpret if the physical units of the individual images are in question. This demand for a high degree of radiometric certainty has to date inhibited this data stream from reaching its full potential as an oceanographic tool. Therefore for newly developed algorithms to be of greater use, there is a long standing and unresolved need for hyperspectral data having a high confidence in absolute radiometric calibration. See, R. O. Green, “Spectral calibration requirement for Earth-looking imaging spectrometers in the solar-reflected spectrum,” Applied Optics 37(4)., 683-690 (1998).